LAUSR

I nterest in behavioral economics has grown steadily within health care. Policy makers, payers, and providers now recognize that the decisions of patients and of their doctors frequently deviate from the strictly Brational^ choices that classical economic theory would predict. For example, patients rarely adhere to the medication regimens or health behaviors that would optimize their health outcomes, and clinicians often make decisions that conflict with evidencebased recommendations or even the practices they profess to endorse. The groundbreaking work of psychologist Daniel Kahneman and his collaborator Amos Tversky raised attention to this field, which was accelerated by Kahneman’s 2002 Nobel Prize in economics and his popular 2011 book BThinking Fast and Slow^ which reached a much broader audience. [1] Behavioral economics examines cognitive, psychological, and cultural factors that may influence how we make decisions, resulting in behavior that another Nobel laureate, economist Richard Thaler, has termed Bpredictably irrational.^ Principles from behavioral economics have been adopted to health care, including the role of heuristics (rules of thumb), the importance of framing, and the effects of specific cognitive biases (for example, overconfidence and status quo bias) [2]. These principles have been incorporated into interventions that seek to use these insights to change health-related behaviors—these include nudges, where systems are redesigned to make the preferred choice the default choice (for example, making generic versions the default in electronic prescribing); incentive programs that reward patients for taking their medications on schedule or getting preventive interventions like immunizations; and specific interventions aimed at how clinicians respond to information or make decisions. The problem of antibiotic overprescribing would seem to be ripe for application of behavioral economics. Acute respiratory infections (ARIs) are one of the most frequent causes of medical visits. Since most ARIs are the result of viral infections, antibiotic prescriptions not only are ineffective but they also carry risks both to the individual in the form of antibiotic side effects, and to society by increasing antibiotic resistance. For over a decade, guidelines from CDC and professional societies have counseled caution in prescribing antibiotics in the outpatient setting, yet recent studies indicate overuse remains prevalent—in one recent study, 40% of patients presenting with ARI received antibiotics, and up to half had no clinical indication supporting antibiotic use [3]. Behavioral economics offers insights into why in practice many clinicians do not follow what they would otherwise endorse as good practice. Clinicians may overestimate their ability to diagnose serious infection, they may incorrectly assume patients will be unhappy if not given antibiotics or underestimate their adverse effects, and under time pressure they may default to reflex behaviors that favor prescribing. Individual habits or beliefs appear to play a strong role—a recent VA study documented a wide variation in clinician ARI practice, with individual antibiotic prescribing rates ranging from 40% to over 95% of patients presenting with ARI [4]. Despite the persistent problem of overprescribing, interventions can make a difference. A recent Cochrane Overview identified 8 systematic reviews and 44 RCTs identifying a variety of interventions that reduce prescribing without worsening patient satisfaction or symptom duration [5]. In this issue, Gong et al. [6] examine the costs and outcomes of different interventions in one of these trials, the Behavioral Economics to Improve Treatment of Acute Respiratory Infections (BEARI) [7]. BEARI was a cluster randomized trial comparing three approaches based on behavioral economic principles—(1) computerized decision support suggesting non-antibiotic alternatives; (2) accountable justification requiring clinicians to document reasons for prescribing antibiotics; and (3) peer comparisons, where periodic emails notify individual clinicians of how they compare to their peers in prescribing. Each of these interventions produced comparable reductions (16–18%) in inappropriate prescribing over an 18month period. Gong used a Markov model to estimate the societal costs and outcomes compared to a baseline practice of provider education. Given comparable reductions in antibiotic use, it is not surprising that each intervention also improved health outcomes by similar amounts, primarily by reducing adverse effects of unnecessary antibiotics. When total societal costs are considered, savings from fewer Published online March 15, 2019